It will allow you quickly assess the future of the keywords you’re using and draw comparisons. You want that trend-line going up!

Another neat use-case for Google Trends is to understand difference in search trends between Web Search, Image Search, YouTube (as well as YouTube keyword research), News Search, and Shopping Search.

Google Trends is also pretty useful for understanding any changes in search behavior. For example if the website that you’re doing SEO for just ran a giant television advertising campaign last month, you’re likely to see a spike.

This tool is a bit of a replacement (or addition depending on how you use it) to the classic Ubersuggest, a stable of SEO keyword research tools. KeywordTool.io, like Ubersuggest, collects keywords from autosuggest:

Ubersuggest will give you autocomple suggestions based on language and region for Web, Images, News, Shopping, Video, and Recipes from Google.

KeywordTool.io similarly will allow you to select language, region, and video (YouTube tab), like Ubersuggest. It also will provide suggestions from Bing and from the Apple AppStore.

Another Google autosuggestion tools I sometimes use is the tool created by agency, Promediacorp. I’m not sure how its methodology differs from Ubersuggest or KeywordTool.io, but I like the results it gives and I use it in addition.

In addition to its good results, it ranks suggestions by order of appearance, allows to export to CSV, and attempts to do a sentiment analysis on the queries.

Note: I use functionality provided within Bing Ads Intelligence that allows you to multiply keywords together. This can also be done within the Google Keyword Planner or with Merge Words. If you don’t end up using Bing Ads Intelligence, use Merge Words.

Knime is a modular data analytics, reporting, and workflow integration platform popular within the big data and machine learning communities. (my best foot forward when it comes to explaining this thing).

It’s open source software and sports drag and drop nodes and has integration with everything from the Twitter and Google APIs to Python and R.

Now, the applications of Knime are pretty much endless, but lately, I’ve been using it specifically for keyword research. Let me explain…

For a while now, I’ve been interested in using social media for keyword research.

It’s my belief that the user wants to consume content that speaks there language, and there is no place better to mine natural language than social media.

At first I tried a premium tool called Tellagence Discover, which I would totally recommend if you have the budget for it, and Knime is too daunting. What it does is pull in something like 200,000 tweets around a specified keyword and maps out relationship of other terms being using around it.

That’s essentially what I was able to create with Knime, but it does more. You input an XML Sitemap for your website.

Then you input the keyword you want to search on Twitter for, input the number of tweets to pull from Twitter, and select whether you want to examine only “popular tweets” or “recent tweets”.

Then it pulls in the text contents from the body of the webpages extracted form the XML sitemap using the Readability API, calculates the frequency that they are mentioned, and pulls in search volume data from Grepwords (not free).

It does the same thing for the Tweets. It extract keywords, calculates frequency, and pulls in data from Grwepwords.

Keywords are then identified by source, either website or twitter, and everything is dumped into an Excel spreadsheet, all automatically.

The end result looks something like this:

For reference, the Knime workflow looks like this:

Click here to see a larger version of my Knime Twitter + Website keyword research workflow.

I am a proponent of doing persona-based keyword research (works especially well with my Knime workflow) and Facebook Ads are pretty useful as a free tool, for helping you create those personas.

So if I was trying to market a dog food company, I might want to explore a persona by gender.

Here are men in the US between the ages of 18 and 65, interested in Pets, and are classified as “People in households that are top grocery spenders” according to “Loyalty card and transaction-level household purchase data with multi-channel coverage across all product categories”.

We can see that this would reach 1,720,000 people.

Now if we explore women with the same criteria…

…we can see that it will reach 4,400,000 people.

So, we should probably consider making our persona character a woman.

Update: I just used SEOChat’s Keyword Suggest Tool for a client, which allows you to drill down into “more longtails” keyword including Amazon as a source. I also used a tool called Serpstat that allows you to filter by “only questions” for keywords.

Happy researching keywords!

These are the major (free) tools I use for keyword research. I’m sure there are more, but I don’t actually use them 🙂

I’ll be back with another post later to explore some free tools I use for content ideation, SEO audits, link building, data analysis, and more!

I hope all is well. Great post, thank you for sharing.
I am very interested in using KNIME with Google Trends data. Are you aware of any Google Trends node that I can use? I found this API on the internet, but I have no idea how to install it in KNIME. Any help on this will be very much appreciated.
Best Regards,

I have one, but I unfortunately can’t share it. There isn’t an official Trends API, but there are a number of unofficial hacks if you search around on GitHub. I recommend checking that out, and then building something out with the Java or Python nodes. I suspect it’s easier than you think.

Thank you for the post Paul, You are absolutely right, there are lots of shitty free tools that doesnt work at all!! have you ever heard of a free tool for broken backlinks??, I found a few but none of them worked at all thank you, I am subscribing to your future posts right now!!